Explore AI Agent Skills & Claude Prompts
Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.
Enter through keywords, occupations, creators, and GitHub sources to see what kinds of skills are emerging across domains.
Use the same catalog through the API
Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
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qdii-fund-health-check
by aifinlab面向基金投研分析领域的QDII体检任务Skill，围绕「QDII基金体检助手」场景提供信息抽取、结构化分析与结果输出。
akshare-qfii
by aifinlabQFII/QDII数据Skill - 提供QFII持仓、QDII基金、外资动向分析 via AkShare
zhitu-data
by aifinlab智兔数服数据Skill - 免注册A股/港股/基金实时行情、历史K线、技术指标 via 智兔数服
yfinance-global
by aifinlab全球股票行情数据工具。优先使用国内数据源(腾讯财经)，支持A股、港股、美股和全球指数。自动回退到Yahoo Finance获取海外数据。
financial-futures-analyzer
by aifinlab金融期货专项分析工具。分析股指期货（IF、IC、IH、IM）、国债期货（T、TF、TS）等金融衍生品。获取基差、贴水率、期现价差、持仓结构等。使用AkShare期货数据、中金所数据。适用于对冲策略、期现套利、资产配置。
supply-chain-customer-identification-assistant
by aifinlab当用户需要在银行对公金融场景下开展供应链金融客户识别、核心企业链路梳理、上下游客户筛选、 交易闭环核验、客户分层、准入判断、白名单初筛、拓客名单整理、风险提示或识别报告输出时， 使用本技能。 适用于围绕核心企业开展供应商、经销商、承运商、服务商等链上客户识别与分类的任务。 当用户提供企业名单、交易资料、发票/订单/合同/物流/回款信息、工商信息、行业资料、 核心企业名单或希望生成供应链客户识别报告、客户清单、准入建议、补充核验事项时，优先调用本技能。
bank-t122-corporate-finance-creditdue-diligence-rm-assistant
by aifinlab当用户需要在银行对公金融场景下，以客户经理视角推进企业授信尽调、整理资料清单、设计访谈提纲、识别现场核查重点、形成风险假设和输出尽调纪要时使用本技能。适合输出能直接支持客户拜访和授信推进的尽调包。
bank-t160-retail-finance-pre-check-assistant
by aifinlabUse when performing retail consumer-loan pre-checks, eligibility screening, and document gap analysis; trigger for requests that need Chinese skill content with structured precheck logic and optional scripts.
bank-t236-transaction-banking-inclusive-finance-admission-assistant
by aifinlab当用户需要在交易银行与普惠金融场景下，对小微客户准入进行初筛、资料核验与方案匹配时使用本技能。
bank-t239-transaction-banking-inclusive-finance-admission-agri-assistant
by aifinlab当用户需要在交易银行与普惠金融场景下，对涉农客户准入进行初筛、资料核验与方案匹配时使用本技能。
bank-t248-transaction-banking-inclusive-finance-bill-discount-assistant
by aifinlab当用户需要在银行交易银行与普惠场景下，围绕票据贴现形成结构化分析、判断和标准化输出时使用本技能。适合输出清晰的输入要求、处理步骤、结果结构和风险边界。
loan-pre-screening-assistant
by aifinlab用于零售金融场景下的个贷预审任务。适用于当用户需要对个人贷款申请进行准入初筛、资料完整性检查、关键风险识别、预审结论分级、补件建议和面谈核验要点整理时使用。 尤其适合住房按揭、消费贷、经营贷、信用贷等场景的贷前预审。 当输入材料不完整、字段前后不一致、存在明显高风险信号或结论需要降级时，应明确标注“待核验”“待补充”“不可直接下正式审批结论”。
Browse Agent Skills by Occupation
23 major groups · 867 SOC occupations
Browse by Category
Explore agent skills organized by their primary use case
Explore the agent skills ecosystem by occupation and creator
SkillMD is not just a keyword search box. It is an open map that organizes public skills by occupation, creator, and repository, helping you see which workflows, judgment criteria, and domain habits people are writing for AI agents.
Then follow creators and GitHub repositories back to the source: compare the skills a team maintains, whether the repo is active, and how the README frames the work before you open, install, or reuse anything.
Use it three ways: learn an unfamiliar field by occupation, study how creators organize skills, then use source context to decide what is worth opening or reusing.
01 Map a field
Browse 23 occupation groups and 867 SOC roles to learn what skills exist in adjacent domains and how they break down real work.
02 Follow creators
Use creator and repository pages to inspect maintained skill collections, recent updates, and source context before trusting a result.
03 Search with sources
Search 1.7M+ collected skills, then use occupation tags, creators, and GitHub source context to decide what is worth opening.
Start with the occupation map, then follow creators and repositories back to real code. SkillMD helps explain why a skill is worth opening, not only what it is named.
Standardizing Agent Capabilities with SKILL.md and Model Context Protocol (MCP)
In the rapidly evolving landscape of artificial intelligence, LLM agents (Large Language Model agents) have transitioned from simple text predictors to autonomous problem solvers. To orchestrate complex, multi-step agentic workflows, developers require a standardized format to specify agent capabilities, prompt instructions, system rules, and database bindings. This is where SKILL.md and the Model Context Protocol (MCP) have emerged as standard developer paradigms. SkillMD serves as the central directory for indexing, exploring, and sharing these critical agent configurations.
Our open-source registry currently tracks over 1.7 million collected SKILL.md configurations and system prompts. By compiling agent configurations from active developers on GitHub, we bridge the gap between prompt engineering research and production execution. Whether you are building agents with Anthropic's Claude Code, OpenAI's GPT-4, Google's Gemini, or local models using Ollama and LlamaIndex, standardized skill definitions ensure your agents behave predictably across different runtime environments.
What is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is an open-source standard designed to connect LLMs to data sources, developer tools, and external environments. MCP establishes a bidirectional communication channel between client applications (like Cursor, Claude Desktop, or custom agent systems) and servers hosting data or capabilities. Standardizing instructions via SKILL.md enables LLMs to query databases, read local files, execute terminal commands, and integrate third-party APIs. SkillMD allows you to find ready-to-run MCP servers and prompt instructions for various occupations and technical tasks.
The Structure of a Professional SKILL.md File
A valid SKILL.md configuration is designed to be easily read by humans and parsed by LLMs. It contains precise system instructions, trigger conditions, required parameters, and execution examples. Below is the typical architectural blueprint of a professional agent skill:
- Metadata & Core Scope: Declares the name of the skill, author details, target models, and a description of the capability.
- Triggers & Intent Detection: Details semantic triggers that help the agent decide when to invoke this skill.
- System Prompts: Explicit system-level instructions that direct the agent's behavior, personality, safety guardrails, and formatting preferences.
- Capabilities & Tools: Lists the files, databases, or APIs the agent must access to complete the tasks.
- Few-Shot Examples: Demonstrates real inputs and outputs, helping the model generalize behavior through in-context learning.
Optimizing Agent Workflows for Modern LLMs
Writing effective agent skills requires deep knowledge of prompt engineering. With the release of advanced reasoning models like Claude 3.5 Sonnet, ChatGPT o1, and DeepSeek-V3, prompt templates must focus on structured thinking. Developers are encouraged to use XML tags (e.g., <thought>, <context>, and <rules>) to isolate execution boundaries. Standardized prompts prevent agents from suffering from context drift, ensuring that long-running tasks remain aligned with the initial system parameters.
Exploring by SOC Occupations and Creator Profiles
What makes SkillMD unique is its taxonomy. Instead of simple text search, we parse and organize files according to the Standard Occupational Classification (SOC) system. This means you can discover skills written for Computer and Mathematical roles, Business and Financial operations, Legal, Design, and and Educational Instruction fields. By tracking creator profiles, developers can study how different teams organize their custom instructions, compare version updates, and fork public configs for specialized enterprise use cases.
SkillMD operates as a high-performance index running on a fast Go backend and a highly responsive Astro SSR frontend. All search queries execute in milliseconds, featuring smart debouncing to prevent multiple API requests while keeping user data secure. Join our community of developers to standardize your AI agent instructions and optimize your LLM prompting workflows today.
Frequently Asked Questions
A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.